## ---- echo=FALSE, results="hide", warning=FALSE-------------------------------
suppressPackageStartupMessages({
  library(ChIPpeakAnno)
  library(rtracklayer)
  library(EnsDb.Hsapiens.v75)
  library(TxDb.Hsapiens.UCSC.hg19.knownGene)
  library(org.Hs.eg.db)
  library(reactome.db)
  library(BSgenome.Hsapiens.UCSC.hg19)
  library(seqinr)
  library(UpSetR)
library(trackViewer)
})
knitr::opts_chunk$set(warning=FALSE, message=FALSE)

## ----workflow1, fig.cap="Venn diagram of overlaps for replicated experiments", fig.width=6, fig.height=6----
library(ChIPpeakAnno)
bed <- system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno")
gr1 <- toGRanges(bed, format="BED", header=FALSE) 
## one can also try import from rtracklayer
gff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
## must keep the class exactly same as gr1$score, i.e., numeric.
gr2$score <- as.numeric(gr2$score) 
ol <- findOverlapsOfPeaks(gr1, gr2)
## add metadata (mean of score) to the overlapping peaks
ol <- addMetadata(ol, colNames="score", FUN=mean) 
ol$peaklist[["gr1///gr2"]][1:2]
makeVennDiagram(ol, fill=c("#009E73", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2"), #circle border color
                cat.col=c("#D55E00", "#0072B2")) # label color, keep same as circle border color

## ----annoData-----------------------------------------------------------------
library(EnsDb.Hsapiens.v75) ##(hg19)
## create annotation file from EnsDb or TxDb
annoData <- toGRanges(EnsDb.Hsapiens.v75, feature="gene")
annoData[1:2]

## ----workflow2,fig.cap="Distribution of peaks around transcript start sites.",fig.width=8,fig.height=6----
overlaps <- ol$peaklist[["gr1///gr2"]]
binOverFeature(overlaps, annotationData=annoData,
               radius=5000, nbins=20, FUN=length, errFun=0,
               xlab="distance from TSS (bp)", ylab="count", 
               main="Distribution of aggregated peak numbers around TSS")

## ----workflow4,fig.cap="Peak distribution over different genomic features.",fig.width=10,fig.height=4----
## check the genomic element distribution of the duplicates
## the genomic element distribution will indicates the 
## the correlation between duplicates.
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
peaks <- GRangesList(rep1=gr1,
                     rep2=gr2)
genomicElementDistribution(peaks, 
                           TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                           promoterRegion=c(upstream=2000, downstream=500),
                           geneDownstream=c(upstream=0, downstream=2000))
## check the genomic element distribution for the overlaps
## the genomic element distribution will indicates the 
## the best methods for annotation.
## The percentages in the legend show the percentage of peaks in 
## each category.
out <- genomicElementDistribution(overlaps, 
                           TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                           promoterRegion=c(upstream=2000, downstream=500),
                           geneDownstream=c(upstream=0, downstream=2000),
                           promoterLevel=list(
                         # from 5' -> 3', fixed precedence 3' -> 5'
                             breaks = c(-2000, -1000, -500, 0, 500),
                             labels = c("upstream 1-2Kb", "upstream 0.5-1Kb", 
                                        "upstream <500b", "TSS - 500b"),
                             colors = c("#FFE5CC", "#FFCA99", 
                                        "#FFAD65", "#FF8E32")))
## check the genomic element distribution by upset plot.
## by function genomicElementUpSetR, no precedence will be considered.
library(UpSetR)
x <- genomicElementUpSetR(overlaps, 
                          TxDb.Hsapiens.UCSC.hg19.knownGene)
upset(x$plotData, nsets=13, nintersects=NA)

## -----------------------------------------------------------------------------
library(trackViewer)
ideo <- loadIdeogram(genome = "hg19")
dataList <- GRangesList(gr1)
ideogramPlot(ideo, dataList, layout = list("chr1", c("chr3", "chr22")), 
             parameterList = list(ideoHeight=unit(.25, "npc")))

## -----------------------------------------------------------------------------
metagenePlot(peaks, TxDb.Hsapiens.UCSC.hg19.knownGene)

## ----workflow3----------------------------------------------------------------
overlaps.anno <- annotatePeakInBatch(overlaps, 
                                     AnnotationData=annoData, 
                                     output="nearestBiDirectionalPromoters",
                                     bindingRegion=c(-2000, 500))
library(org.Hs.eg.db)
overlaps.anno <- addGeneIDs(overlaps.anno,
                            "org.Hs.eg.db",
                            IDs2Add = "entrez_id")
head(overlaps.anno)
write.csv(as.data.frame(unname(overlaps.anno)), "anno.csv")

## ----workflow20,fig.cap="Pie chart of the distribution of common peaks around features."----
pie1(table(overlaps.anno$insideFeature))

## ----enrichment---------------------------------------------------------------
over <- getEnrichedGO(overlaps.anno, orgAnn="org.Hs.eg.db", condense=TRUE)
enrichmentPlot(over)
library(reactome.db)
path <- getEnrichedPATH(overlaps.anno, "org.Hs.eg.db", "reactome.db", maxP=.05)
enrichmentPlot(path)

## ----fasta--------------------------------------------------------------------
library(BSgenome.Hsapiens.UCSC.hg19)
seq <- getAllPeakSequence(overlaps, upstream=20, downstream=20, genome=Hsapiens)
write2FASTA(seq, "test.fa")

## ----consensus,fig.cap="Histogram of Z-score of 6-mer",fig.height=6,fig.width=6----
## summary of the short oligos
library(seqinr)
freqs <- oligoFrequency(Hsapiens$chr1, MarkovOrder=3)
os <- oligoSummary(seq, oligoLength=6, MarkovOrder=3, 
                   quickMotif=FALSE, freqs=freqs)
## plot the results
zscore <- sort(os$zscore)
h <- hist(zscore, breaks=100, xlim=c(-50, 50), main="Histogram of Z-score")
text(zscore[length(zscore)], max(h$counts)/10, 
     labels=names(zscore[length(zscore)]), adj=1)

## ----simulation, fig.cap="Histogram of Z-score of simulation data", fig.width=6, fig.height=6----
## We can also try simulation data
seq.sim.motif <- list(c("t", "g", "c", "a", "t", "g"), 
                      c("g", "c", "a", "t", "g", "c"))
set.seed(1)
seq.sim <- sapply(sample(c(2, 1, 0), 1000, replace=TRUE, prob=c(0.07, 0.1, 0.83)), 
                  function(x){
    s <- sample(c("a", "c", "g", "t"), 
                sample(100:1000, 1), replace=TRUE)
    if(x>0){
        si <- sample.int(length(s), 1)
        if(si>length(s)-6) si <- length(s)-6
        s[si:(si+5)] <- seq.sim.motif[[x]]
    }
    paste(s, collapse="")
})
os <- oligoSummary(seq.sim, oligoLength=6, MarkovOrder=3, 
                   quickMotif=TRUE)
zscore <- sort(os$zscore, decreasing=TRUE)
h <- hist(zscore, breaks=100, main="Histogram of Z-score")
text(zscore[1:2], rep(5, 2), 
     labels=names(zscore[1:2]), adj=0, srt=90)

## ----simulation.motif, fig.cap="Motif of simulation data", fig.width=6, fig.height=6----
## generate the motifs
library(motifStack)
pfms <- mapply(function(.ele, id)
    new("pfm", mat=.ele, name=paste("SAMPLE motif", id)), 
    os$motifs, 1:length(os$motifs))
motifStack(pfms[[1]])

## ----peaksNearBDP16-----------------------------------------------------------
bdp <- peaksNearBDP(overlaps, annoData, maxgap=5000)
c(bdp$percentPeaksWithBDP, 
  bdp$n.peaks, 
  bdp$n.peaksWithBDP)
bdp$peaksWithBDP[1:2]

## ----findEnhancers------------------------------------------------------------
DNA5C <- system.file("extdata", 
                     "wgEncodeUmassDekker5CGm12878PkV2.bed.gz",
                     package="ChIPpeakAnno")
DNAinteractiveData <- toGRanges(gzfile(DNA5C))
findEnhancers(overlaps, annoData, DNAinteractiveData)

## ----importData---------------------------------------------------------------
path <- system.file("extdata", package="ChIPpeakAnno")
files <- dir(path, "broadPeak")
data <- sapply(file.path(path, files), toGRanges, format="broadPeak")
names(data) <- gsub(".broadPeak", "", files)

## ----vennDiagram, fig.cap="Venn diagram of overlaps.", fig.width=6, fig.height=6----
ol <- findOverlapsOfPeaks(data, connectedPeaks="keepAll")
averagePeakWidth <- mean(width(unlist(GRangesList(ol$peaklist))))
tot <- ceiling(3.3e+9 * .03 / averagePeakWidth / 24)
makeVennDiagram(ol, totalTest=tot, connectedPeaks="keepAll", 
                fill=c("#CC79A7", "#56B4E9", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2", "#E69F00"), #circle border color
                cat.col=c("#D55E00", "#0072B2", "#E69F00"))
## see the difference if we set connectedPeaks to "keepFirstListConsistent"
## set connectedPeaks to keepFirstListConsistent will show consistent total 
## number of peaks for the first peak list.
makeVennDiagram(ol, totalTest=tot, connectedPeaks="keepFirstListConsistent", 
                fill=c("#CC79A7", "#56B4E9", "#F0E442"),
                col=c("#D55E00", "#0072B2", "#E69F00"),
                cat.col=c("#D55E00", "#0072B2", "#E69F00"))


## ----peakPermTest1, fig.cap="permutation test for YY1 and TEAD4"--------------
    data(HOT.spots)
    data(wgEncodeTfbsV3)
    hotGR <- reduce(unlist(HOT.spots))
    removeOl <- function(.ele){
        ol <- findOverlaps(.ele, hotGR)
        if(length(ol)>0) .ele <- .ele[-unique(queryHits(ol))]
        .ele
    }
    TAF <- removeOl(data[["TAF"]])
    TEAD4 <- removeOl(data[["Tead4"]])
    YY1 <- removeOl(data[["YY1"]])
    # we subset the pool to save demo time
    set.seed(1)
    wgEncodeTfbsV3.subset <- 
        wgEncodeTfbsV3[sample.int(length(wgEncodeTfbsV3), 2000)]
    pool <- new("permPool", grs=GRangesList(wgEncodeTfbsV3.subset), N=length(YY1))
    pt1 <- peakPermTest(YY1, TEAD4, pool=pool, seed=1, force.parallel=FALSE)
    plot(pt1)

## ----peakPermTest2, fig.cap="permutation test for YY1 and TAF"----------------
    pt2 <- peakPermTest(YY1, TAF, pool=pool, seed=1, force.parallel=FALSE)
    plot(pt2)

## ----heatmap,fig.cap="Heatmap of aligned features sorted by signal of TAF",fig.width=4,fig.height=6----
features <- ol$peaklist[[length(ol$peaklist)]]
feature.recentered <- reCenterPeaks(features, width=4000)
## here we also suggest importData function in bioconductor trackViewer package 
## to import the coverage.
## compare rtracklayer, it will save you time when handle huge dataset.
library(rtracklayer)
files <- dir(path, "bigWig")
if(.Platform$OS.type != "windows"){
    cvglists <- sapply(file.path(path, files), import, 
                       format="BigWig", 
                       which=feature.recentered, 
                       as="RleList")
}else{## rtracklayer can not import bigWig files on Windows
    load(file.path(path, "cvglist.rds"))
}
names(cvglists) <- gsub(".bigWig", "", files)
feature.center <- reCenterPeaks(features, width=1)
sig <- featureAlignedSignal(cvglists, feature.center, 
                            upstream=2000, downstream=2000)
##Because the bw file is only a subset of the original file,
##the signals are not exists for every peak.
keep <- rowSums(sig[[2]]) > 0
sig <- sapply(sig, function(.ele) .ele[keep, ], simplify = FALSE)
feature.center <- feature.center[keep]
heatmap <- featureAlignedHeatmap(sig, feature.center, 
                                 upstream=2000, downstream=2000,
                                 upper.extreme=c(3,.5,4))

## ----sortHeatmapByHcluster,fig.cap="Heatmap of aligned features sorted by hclut",fig.width=4,fig.height=6----
sig.rowsums <- sapply(sig, rowSums, na.rm=TRUE)
d <- dist(sig.rowsums)
hc <- hclust(d)
feature.center$order <- hc$order
heatmap <- featureAlignedHeatmap(sig, feature.center, 
                                 upstream=2000, downstream=2000,
                                 upper.extreme=c(3,.5,4),
                                 sortBy="order")

## ----distribution,fig.cap="Distribution of aligned features",fig.width=6,fig.height=6----
featureAlignedDistribution(sig, feature.center, 
                           upstream=2000, downstream=2000,
                           type="l")

